Statistics Colloquium: STA 290
Tuesday, January 21st at 4:10pm, MSB 1147 (Colloquium Room) Refreshments at 3:30pm in MSB 4110 (4th floor lounge)
Speaker: Susan Wei, University of North Carolina, Chapel Hill
Title: "Latent Supervised Learning"
Abstract: Machine learning is a branch of artificial intelligence concerning the construction of systems that can learn from data. Machine learning algorithms can be placed along a spectrum according to the type of input available during training. The two main machine learning algorithms, unsupervised and supervised learning, occupy either end of this spectrum.
In this talk I will overview some of my recent research on machine learning tasks that fall somewhere in the middle of this spectrum. I will primarily focus on a new machine learning task called latent supervised learning, where the goal is to learn a binary classifier from continuous training labels that serve as surrogates for the unobserved class labels. A specific model is investigated where the surrogate variable arises from a two-component Gaussian mixture with unknown means and variances, and the component membership is determined by a hyperplane in the covariate space. A data-driven sieve maximum likelihood estimator for the hyperplane is proposed, which in turn can be used to estimate the parameters of the Gaussian mixture. Extensions of the framework to survival data and applications to estimating treatment effect heterogeneity will also be discussed.